Investigating the Nexus between Stock Market and Institutional Quality in Emerging Markets and Developing Countries: A Panel Data Analysis
Bibliographic record
Abstract
Purpose: This study empirically explores the impact of institutional quality on the stock market in Emerging Markets and Developing Countries (EM&DC), study utilizes individual institutional quality indicators as well as aggregated in the form of an institutional quality index and checks their impact on the stock market. Methodology: The study employs a sample of 43 Emerging Markets and Developing Countries for the time duration from 1996 to 2022. By applying the Generalized method of moments (GMM) and Principal Component Analysis (PCA). Findings: The result shows that Institutional quality is not good in these countries and plays a detrimental role in association with the stock market. Mostly individual indicators of institutional quality have a negative and significant impact on the stock market except voice and accountability have a positive and significant impact on the stock market. We also construct an Institutional quality index by applying Principal component analysis (PCA), which also has a negative impact on the stock market. Implications: The findings underscore the importance of institutional factors in shaping the financial development of nations. Through our analysis, it becomes evident that emerging markets and developing countries (EM&DCs) should prioritize enhancing their institutional frameworks, as these play a pivotal role in influencing financial development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".